Telehealth Utilization in Oral Medicine and Oral and Maxillofacial Surgery.

Objectives: The objectives of this retrospective study were to analyze telehealth utilization for two specialty care practices: oral medicine (OM) and oral and maxillofacial surgery (OMFS) during the first 2 years of the pandemic, its impact as a new treatment modality and on participating providers, as well as identify the type of patient visit that most readily adopted telehealth. Methods: Retrospective study of patients who sought specialty services, OM and OMFS, at an outpatient clinic in a university health system setting between March 1, 2019, and February 28, 2022. Source data were obtained from Epic, an electronic medical record application. Data were graphed using Tableau and Microsoft Excel software. Statistical analysis was performed utilizing chi-squared test and analysis of variance (ANOVA). Results: OMFS utilized telehealth 12% of the time, and OM 8% of the time. The majority (87%) of telehealth visits were for return patients (RPs). Compared with the first year of the pandemic, there was a decrease in the number of telehealth visits in the second year (p = 0.0001). As of August 2022, new patient (NP) telehealth encounters have largely returned to prepandemic levels (0-1.5%), whereas RP telehealth visits remained at an average level of 11.4% (9.4-12.4%). Surveyed providers consider telehealth as an effective complement to in-person care and will continue its use (4.2/5 Likert scale). Conclusions: Telehealth has become a viable pathway of care for OM and OMFS who previously did not utilize the remote platform to deliver healthcare. As a new treatment modality, telehealth is perceived as impactful in increasing access to specialty care by participating providers. NP visits are now almost completely in person, but telehealth continues for RPs. Ongoing demand for telehealth highlights urgency to develop appropriate standards and effective remote diagnostic/monitoring tools to maximize telehealth's capability to leverage finite health care resources and increase access to specialty care.

A Novel Ergonomic Curette Design Reduces Dental Prophylaxis-Induced Muscle Work and Fatigue.

BACKGROUND: To compare fatigue, comfort, and muscle work associated with the use of two periodontal curettes during scaling: one with a novel adaptive design, the other with a conventional non-adaptive design. METHODS: Twelve hygienists scaled a typodont using two Universal Barnhart 5/6 curettes: (1) a prototype featuring an adaptive silicone-covered handle (Curette A), and (2) a stainless-steel curette (Curette B). Surface Electromyography (sEMG) traced muscle work. Hand positions, fatigue, comfort, pinch, and grasp strength were recorded. Paired t-tests and a repeated measures ANOVA with covariates were tested for differences. The significance level was set at p < 0.05. RESULTS: Curette A performed significantly better in all categories. Pinch and grasp strength and fatigue were significantly reduced post-instrumentation for Curette B. Curette A required significantly less (i) total muscle work and (ii) work in individual muscles. Comfort, correct grasp, and blade adaptation were significantly better using Curette A. CONCLUSIONS: A curette featuring a novel adaptive handle design demonstrated significantly improved ergonomic performance. Additional clinical studies are needed to solidify our understanding of the potential short- and long-term benefits of the novel curette handle design. PRACTICAL IMPLICATIONS: A novel adaptive curette handle design that enables the clinician to adapt the instrument across the index finger may reduce musculoskeletal burden and fatigue, as well as improve comfort during periodontal instrumentation.

Real-Time PCR Detection of Candida Species in Biopsy Samples from Non-Smokers with Oral Dysplasia and Oral Squamous Cell Cancer: A Retrospective Archive Study.

The impact of Candida sp. in the development of oral cancer remains uncertain and requires sensitive analytical approaches for clarification. Given the invasive capabilities of these microorganisms in penetrating and invading host tissues through hyphal invasion, this study sought to detect the presence of five Candida sp. in oral biopsy tissue samples from non-smoker patients. Samples were obtained from patients at varying stages of oral carcinogenesis, including dysplasia, carcinoma in situ, OSCC, and histologically benign lesions, and analyzed using Real-Time PCR. Oral tissue samples from 80 patients (46 males and 34 females) were included. Significantly higher C. albicans presence was detected in the mild/moderate dysplasia group compared to the healthy (p = 0.001), carcinoma in situ (p = 0.031) and OSCC groups (p = 0.000). Similarly, C. tropicalis carriage was higher in tissues with mild/moderate dysplasia compared to healthy (p = 0.004) and carcinoma in situ (p = 0.019). Our results showed a significant increase in the presence of C. albicans and C. tropicalis within the mild/moderate dysplasia group compared to other cohorts. Coexistence of these two microorganisms was observed, suggesting a potential transition from a commensal state to an opportunistic pathogen, which could be particularly linked to the onset of oral neoplasia.

Compliance with Specialist Referral for Increased Cancer Risk in Low-Resource Settings: In-Person vs. Telehealth Options.

Efforts are underway to improve the accuracy of non-specialist screening for oral cancer (OC) risk, yet better screening will only translate into improved outcomes if at-risk individuals comply with specialist referral. Most individuals from low-resource, minority, and underserved (LRMU) populations fail to complete a specialist referral for OC risk. The goal was to evaluate the impact of a novel approach on specialist referral compliance in individuals with a positive OC risk screening outcome. A total of 60 LRMU subjects who had screened positive for increased OC risk were recruited and given the choice of referral for an in-person (20 subjects) or a telehealth (40 subjects) specialist visit. Referral compliance was tracked weekly over 6 months. Compliance was 30% in the in-person group, and 83% in the telehealth group. Approximately 83-85% of subjects from both groups who had complied with the first specialist referral complied with a second follow-up in-person specialist visit. Overall, 72.5% of subjects who had chosen a remote first specialist visit had entered into the continuum of care by the study end, vs. 25% of individuals in the in-person specialist group. A two-step approach that uses telehealth to overcome barriers may improve specialist referral compliance in LRMU individuals with increased OC risk.

A randomized double-blind clinical trial evaluating comparative plaque and gingival health associated with commercially available stannous fluoride-containing dentifrices as compared to a sodium fluoride control dentifrice.

BACKGROUND: Gingivitis is a non-specific inflammatory lesion in response to the accumulation of oral biofilm and is a necessary precursor to periodontitis. Enhanced oral hygiene practices, including utilization of a dentifrice that could significantly improve plaque accumulation and gingival inflammation, is desirable to prevent and treat gingivitis and potentially prevent progression to periodontitis. This clinical study aimed to investigate the effect of a new stannous fluoride-containing dentifrice with 2.6% ethylenediamine tetra acetic acid (EDTA) as an anti-tartar agent to reduce plaque index and gingival index over a 3-month study period compared to other commercially-available fluoride-containing dentifrices. METHODS: This double-blind, randomized controlled clinical study evaluated plaque, gingival inflammation, and sulcular bleeding in patients using one of five commercially available fluoride-containing dentifrices The dentifrices tested contained: 0.454% stannous fluoride and 2.6% EDTA (D1), 0.24% sodium fluoride (C), and 0.454% stannous fluoride (D2-D4). One hundred fifty subjects participated over a 3-month period. Co-primary endpoints were improvements in plaque index (PI) and modified gingival index (mGI) from baseline values. No professional cleaning was performed during the study period. RESULTS: All subjects in the study demonstrated statistically significant improvements in all measures of oral hygiene over the 3-month study period. Subjects using dentifrice 1 (D1) showed statistically significantly greater reductions in PI, mGI, and modified sulcular bleeding index (mSBI) compared with all other commercially-available dentifrices tested (p < 0.00001). CONCLUSIONS: A new dentifrice with 0.454% stannous fluoride and 2.6% EDTA demonstrated significant improvements in clinical parameters associated with gingivitis compared to other sodium and stannous fluoride containing dentifrices.

Interpretable and Reliable Oral Cancer Classifier with Attention Mechanism and Expert Knowledge Embedding via Attention Map.

Convolutional neural networks have demonstrated excellent performance in oral cancer detection and classification. However, the end-to-end learning strategy makes CNNs hard to interpret, and it can be challenging to fully understand the decision-making procedure. Additionally, reliability is also a significant challenge for CNN based approaches. In this study, we proposed a neural network called the attention branch network (ABN), which combines the visual explanation and attention mechanisms to improve the recognition performance and interpret the decision-making simultaneously. We also embedded expert knowledge into the network by having human experts manually edit the attention maps for the attention mechanism. Our experiments have shown that ABN performs better than the original baseline network. By introducing the Squeeze-and-Excitation (SE) blocks to the network, the cross-validation accuracy increased further. Furthermore, we observed that some previously misclassified cases were correctly recognized after updating by manually editing the attention maps. The cross-validation accuracy increased from 0.846 to 0.875 with the ABN (Resnet18 as baseline), 0.877 with SE-ABN, and 0.903 after embedding expert knowledge. The proposed method provides an accurate, interpretable, and reliable oral cancer computer-aided diagnosis system through visual explanation, attention mechanisms, and expert knowledge embedding.

Novel Approach to Improving Specialist Access in Underserved Populations with Suspicious Oral Lesions.

Late detection and specialist referral result in poor oral cancer outcomes globally. High-risk LRMU populations usually do not have access to oral medicine specialists, a specialty of dentistry, whose expertise includes the identification, treatment, and management of oral cancers. To overcome this access barrier, there is an urgent need for novel, low-cost tele-health approaches to expand specialist access to low-resource, remote and underserved individuals. The goal of this study was to compare the diagnostic accuracy of remote versus in-person specialist visits using a novel, low-cost telehealth platform consisting of a smartphone-based, remote intraoral camera and custom software application. A total of 189 subjects with suspicious oral lesions requiring biopsy (per the standard of care) were recruited and consented. Each subject was examined, and risk factors were recorded twice: once by an on-site specialist, and again by an offsite specialist. A novel, low-cost, smartphone-based intraoral camera paired with a custom software application were utilized to perform synchronous remote video/still imaging and risk factor assessment by the off-site specialist. Biopsies were performed at a later date following specialist recommendations. The study's results indicated that on-site specialist diagnosis showed high sensitivity (94%) and moderate specificity (72%) when compared to histological diagnosis, which did not significantly differ from the accuracy of remote specialist telediagnosis (sensitivity: 95%; specificity: 84%). These preliminary findings suggest that remote specialist visits utilizing a novel, low-cost, smartphone-based telehealth tool may improve specialist access for low-resource, remote and underserved individuals with suspicious oral lesions.

Better living through chemistry? A randomized, double-blind controlled study evaluating the efficacy of plaque control and gingival health impacts of a novel stannous fluoride-containing gel.

BACKGROUND: Gingivitis is a nonspecific inflammatory lesion in response to the accumulation of oral biofilm and is a necessary precursor to periodontitis. Enhanced oral hygiene practices are necessary to reverse gingivitis and a dentifrice that could provide significant clinical reductions in plaque accumulation and gingival inflammation would be desirable to treat gingivitis and potentially prevent progression to periodontitis. This clinical study aimed to investigate the effect of a novel stannous fluoride-containing dentifrice with 2.6% ethylenediamine tetra-acetic acid (EDTA) as an antitartar agent to reduce Plaque Index (PI) and Gingival Index over a 3-month study period. METHODS: This double-blind, randomized controlled clinical study evaluated plaque, gingival inflammation, and sulcular bleeding in patients using either a novel dental gel containing 0.454% stannous fluoride and 2.6% EDTA or a dentifrice with 0.24% sodium fluoride. Sixty subjects participated over a 3-month period. Co-primary endpoints were improvements in PI and Modified Gingival Index (mGI) from baseline values. No professional cleaning was performed during the study period. RESULTS: All subjects in the study demonstrated statistically significant improvements in all measures of oral hygiene over the 3-month study period. Subjects using the novel dental gel showed statistically significantly greater reductions in PI (ΔPI) [(-1.43 ± 0.34; -0.49 ± 0.13) (p < 0.00001)], mGI (ΔmGI) [(-1.11 ± 0.22; -0.16 ± 0.12) (p < 0.00001)], and modified sulcular bleeding index (ΔmSBI) [(-1.15 ± 0.18; -0.20 ± 0.07) (p < 0.00001)]. CONCLUSIONS: The novel dental gel demonstrated significant improvements in clinical parameters associated with gingivitis compared to a commercially available sodium fluoride dentifrice.

Exploring uncertainty measures in convolutional neural network for semantic segmentation of oral cancer images.

Significance: Oral cancer is one of the most prevalent cancers, especially in middle- and low-income countries such as India. Automatic segmentation of oral cancer images can improve the diagnostic workflow, which is a significant task in oral cancer image analysis. Despite the remarkable success of deep-learning networks in medical segmentation, they rarely provide uncertainty quantification for their output. Aim: We aim to estimate uncertainty in a deep-learning approach to semantic segmentation of oral cancer images and to improve the accuracy and reliability of predictions. Approach: This work introduced a UNet-based Bayesian deep-learning (BDL) model to segment potentially malignant and malignant lesion areas in the oral cavity. The model can quantify uncertainty in predictions. We also developed an efficient model that increased the inference speed, which is almost six times smaller and two times faster (inference speed) than the original UNet. The dataset in this study was collected using our customized screening platform and was annotated by oral oncology specialists. Results: The proposed approach achieved good segmentation performance as well as good uncertainty estimation performance. In the experiments, we observed an improvement in pixel accuracy and mean intersection over union by removing uncertain pixels. This result reflects that the model provided less accurate predictions in uncertain areas that may need more attention and further inspection. The experiments also showed that with some performance compromises, the efficient model reduced computation time and model size, which expands the potential for implementation on portable devices used in resource-limited settings. Conclusions: Our study demonstrates the UNet-based BDL model not only can perform potentially malignant and malignant oral lesion segmentation, but also can provide informative pixel-level uncertainty estimation. With this extra uncertainty information, the accuracy and reliability of the model's prediction can be improved.

Interpretable deep learning approach for oral cancer classification using guided attention inference network.

SIGNIFICANCE: Convolutional neural networks (CNNs) show the potential for automated classification of different cancer lesions. However, their lack of interpretability and explainability makes CNNs less than understandable. Furthermore, CNNs may incorrectly concentrate on other areas surrounding the salient object, rather than the network's attention focusing directly on the object to be recognized, as the network has no incentive to focus solely on the correct subjects to be detected. This inhibits the reliability of CNNs, especially for biomedical applications. AIM: Develop a deep learning training approach that could provide understandability to its predictions and directly guide the network to concentrate its attention and accurately delineate cancerous regions of the image. APPROACH: We utilized Selvaraju et al.'s gradient-weighted class activation mapping to inject interpretability and explainability into CNNs. We adopted a two-stage training process with data augmentation techniques and Li et al.'s guided attention inference network (GAIN) to train images captured using our customized mobile oral screening devices. The GAIN architecture consists of three streams of network training: classification stream, attention mining stream, and bounding box stream. By adopting the GAIN training architecture, we jointly optimized the classification and segmentation accuracy of our CNN by treating these attention maps as reliable priors to develop attention maps with more complete and accurate segmentation. RESULTS: The network's attention map will help us to actively understand what the network is focusing on and looking at during its decision-making process. The results also show that the proposed method could guide the trained neural network to highlight and focus its attention on the correct lesion areas in the images when making a decision, rather than focusing its attention on relevant yet incorrect regions. CONCLUSIONS: We demonstrate the effectiveness of our approach for more interpretable and reliable oral potentially malignant lesion and malignant lesion classification.

Effects of Mouthwash on Oral Cytomegalovirus, Epstein-Barr Virus, Herpes Simplex Virus Type-1.

Background: Cytomegalovirus (HCMV), Epstein-Barr virus (EBV), and herpes simplex virus type-1 (HSV-1) are pathogens. Objectives: The goal of the present double-blinded, randomized study was to compare the effect on oral viral load of twice daily use over 60 days of Lumineux MouthwashRvs. de-ionized water. The main composition of the mouthwash was Dead Sea salt. Methods: 30 participants were randomized to test or control. For 60 days, participants rinsed for 60s twice daily with 20ml of their allocated mouthwash, after morning and evening meals. On Day 0 and 60, before eating and oral hygiene and at least 60 minutes after drinking, unstimulated saliva was collected. Samples underwent mRNA analysis. Study endpoints were changes in Log Salivary Viral Load. Result: After adjusting for baseline differences, the reduction in viral load was significantly greater for the test group, all p-values <0.001. Baseline differences did not have an effect on the differences between groups in change over time. Conclusion: After adjusting for baseline differences, the reduction in viral load was significantly greater for the test group, all p-values <0.001. Baseline differences did not have an effect on the differences between groups in change over time.

Bayesian deep learning for reliable oral cancer image classification.

In medical imaging, deep learning-based solutions have achieved state-of-the-art performance. However, reliability restricts the integration of deep learning into practical medical workflows since conventional deep learning frameworks cannot quantitatively assess model uncertainty. In this work, we propose to address this shortcoming by utilizing a Bayesian deep network capable of estimating uncertainty to assess oral cancer image classification reliability. We evaluate the model using a large intraoral cheek mucosa image dataset captured using our customized device from high-risk population to show that meaningful uncertainty information can be produced. In addition, our experiments show improved accuracy by uncertainty-informed referral. The accuracy of retained data reaches roughly 90% when referring either 10% of all cases or referring cases whose uncertainty value is greater than 0.3. The performance can be further improved by referring more patients. The experiments show the model is capable of identifying difficult cases needing further inspection.

Classification of imbalanced oral cancer image data from high-risk population.

SIGNIFICANCE: Early detection of oral cancer is vital for high-risk patients, and machine learning-based automatic classification is ideal for disease screening. However, current datasets collected from high-risk populations are unbalanced and often have detrimental effects on the performance of classification. AIM: To reduce the class bias caused by data imbalance. APPROACH: We collected 3851 polarized white light cheek mucosa images using our customized oral cancer screening device. We use weight balancing, data augmentation, undersampling, focal loss, and ensemble methods to improve the neural network performance of oral cancer image classification with the imbalanced multi-class datasets captured from high-risk populations during oral cancer screening in low-resource settings. RESULTS: By applying both data-level and algorithm-level approaches to the deep learning training process, the performance of the minority classes, which were difficult to distinguish at the beginning, has been improved. The accuracy of "premalignancy" class is also increased, which is ideal for screening applications. CONCLUSIONS: Experimental results show that the class bias induced by imbalanced oral cancer image datasets could be reduced using both data- and algorithm-level methods. Our study may provide an important basis for helping understand the influence of unbalanced datasets on oral cancer deep learning classifiers and how to mitigate.

Validation of a Point-of-Care Optical Coherence Tomography Device with Machine Learning Algorithm for Detection of Oral Potentially Malignant and Malignant Lesions.

Non-invasive strategies that can identify oral malignant and dysplastic oral potentially-malignant lesions (OPML) are necessary in cancer screening and long-term surveillance. Optical coherence tomography (OCT) can be a rapid, real time and non-invasive imaging method for frequent patient surveillance. Here, we report the validation of a portable, robust OCT device in 232 patients (lesions: 347) in different clinical settings. The device deployed with algorithm-based automated diagnosis, showed efficacy in delineation of oral benign and normal (n = 151), OPML (n = 121), and malignant lesions (n = 75) in community and tertiary care settings. This study showed that OCT images analyzed by automated image processing algorithm could distinguish the dysplastic-OPML and malignant lesions with a sensitivity of 95% and 93%, respectively. Furthermore, we explored the ability of multiple (n = 14) artificial neural network (ANN) based feature extraction techniques for delineation high grade-OPML (moderate/severe dysplasia). The support vector machine (SVM) model built over ANN, delineated high-grade dysplasia with sensitivity of 83%, which in turn, can be employed to triage patients for tertiary care. The study provides evidence towards the utility of the robust and low-cost OCT instrument as a point-of-care device in resource-constrained settings and the potential clinical application of device in screening and surveillance of oral cancer.

Mobile-based oral cancer classification for point-of-care screening.

SIGNIFICANCE: Oral cancer is among the most common cancers globally, especially in low- and middle-income countries. Early detection is the most effective way to reduce the mortality rate. Deep learning-based cancer image classification models usually need to be hosted on a computing server. However, internet connection is unreliable for screening in low-resource settings. AIM: To develop a mobile-based dual-mode image classification method and customized Android application for point-of-care oral cancer detection. APPROACH: The dataset used in our study was captured among 5025 patients with our customized dual-modality mobile oral screening devices. We trained an efficient network MobileNet with focal loss and converted the model into TensorFlow Lite format. The finalized lite format model is ∼16.3 MB and ideal for smartphone platform operation. We have developed an Android smartphone application in an easy-to-use format that implements the mobile-based dual-modality image classification approach to distinguish oral potentially malignant and malignant images from normal/benign images. RESULTS: We investigated the accuracy and running speed on a cost-effective smartphone computing platform. It takes ∼300 ms to process one image pair with the Moto G5 Android smartphone. We tested the proposed method on a standalone dataset and achieved 81% accuracy for distinguishing normal/benign lesions from clinically suspicious lesions, using a gold standard of clinical impression based on the review of images by oral specialists. CONCLUSIONS: Our study demonstrates the effectiveness of a mobile-based approach for oral cancer screening in low-resource settings.

The contribution of artificial intelligence to reducing the diagnostic delay in oral cancer.

Oral cancer (OC) is the sixth most commonly reported malignant disease globally, with high rates of disease-related morbidity and mortality due to advanced loco-regional stage at diagnosis. Early detection and prompt treatment offer the best outcomes to patients, yet the majority of OC lesions are detected at late stages with 45% survival rate for 2 years. The primary cause of poor OC outcomes is unavailable or ineffective screening and surveillance at the local point-of-care level, leading to delays in specialist referral and subsequent treatment. Lack of adequate awareness of OC among the public and professionals, and barriers to accessing health care services in a timely manner also contribute to delayed diagnosis. As image analysis and diagnostic technologies are evolving, various artificial intelligence (AI) approaches, specific algorithms and predictive models are beginning to have a considerable impact in improving diagnostic accuracy for OC. AI based technologies combined with intraoral photographic images or optical imaging methods are under investigation for automated detection and classification of OC. These new methods and technologies have great potential to improve outcomes, especially in low-resource settings. Such approaches can be used to predict oral cancer risk as an adjunct to population screening by providing real-time risk assessment. The objective of this study is to (1) provide an overview of components of delayed OC diagnosis and (2) evaluate novel AI based approaches with respect to their utility and implications for improving oral cancer detection.

Evaluating efficacy of a novel dentifrice in reducing probing depths in Stage I and II periodontitis maintenance patients: A randomized, double-blind, positive controlled clinical trial.

BACKGROUND: Compliance to periodontal maintenance therapy (PMT) is essential for long-term periodontal health. Between PMT visits, patients must maintain good oral hygiene. A dentifrice with demonstrable clinical benefits for use between PMT visits would be highly desirable. The aim of this clinical study was to investigate the effect of a novel dental gel on probing depths (PD) and inflammation when used as a home care dentifrice in Stage I and II periodontitis patients. METHODS: This double-blind clinical study randomized 65 subjects with Stage I and II periodontitis to the novel dental gel containing 2.6% EDTA, and a commercially available anti-gingivitis dentifrice with 0.454% stannous fluoride. Primary endpoint was PD at 6 months for those sites with baseline PD ≥ 4 mm and secondary endpoints included whole mouth mean scores of modified gingival index (MGI), modified sulcus bleeding index (mSBI) and plaque index (PI). No SRP was performed at baseline. RESULTS: Subjects using the novel dentifrice showed significant PD reductions of 1.18 mm (from 4.27 mm at baseline to 3.09 mm at 6 months) compared to 0.93 mm (from 4.23 mm at baseline to 3.30 mm at 6 months) shown for those using the positive control dentifrice. Difference between treatments at 6 months was 0.21 mm with P-value = 0.0126. Significant improvements in MGI (P = 0.0000), mSBI (P = 0.0000), and PI (P = 0.0102) were also observed in 6 months. CONCLUSION: The novel dentifrice showed significant reductions in PD and gingival inflammation over 6 months solely as a home care dentifrice without baseline SRP in Stage I and II periodontitis maintenance patients.

Small form factor, flexible, dual-modality handheld probe for smartphone-based, point-of-care oral and oropharyngeal cancer screening.

Oral cancer is a growing health issue in low- and middle-income countries due to betel quid, tobacco, and alcohol use and in younger populations of middle- and high-income communities due to the prevalence of human papillomavirus. The described point-of-care, smartphone-based intraoral probe enables autofluorescence imaging and polarized white light imaging in a compact geometry through the use of a USB-connected camera module. The small size and flexible imaging head improves on previous intraoral probe designs and allows imaging the cheek pockets, tonsils, and base of tongue, the areas of greatest risk for both causes of oral cancer. Cloud-based remote specialist and convolutional neural network clinical diagnosis allow for both remote community and home use. The device is characterized and preliminary field-testing data are shared.

Intra-operative point-of-procedure delineation of oral cancer margins using optical coherence tomography.

OBJECTIVES: Surgical margin status is a significant determinant of treatment outcome in oral cancer. Negative surgical margins can decrease the loco-regional recurrence by five-fold. The current standard of care of intraoperative clinical examination supplemented by histological frozen section, can result in a risk of positive margins from 5 to 17 percent. In this study, we attempted to assess the utility of intraoperative optical coherence tomography (OCT) imaging with automated diagnostic algorithm to improve on the current method of clinical evaluation of surgical margin in oral cancer. MATERIALS AND METHODS: We have used a modified handheld OCT device with automated algorithm based diagnostic platform for imaging. Intraoperatively, images of 125 sites were captured from multiple zones around the tumor of oral cancer patients (n = 14) and compared with the clinical and pathologic diagnosis. RESULTS: OCT showed sensitivity and specificity of 100%, equivalent to histological diagnosis (kappa, ĸ = 0.922), in detection of malignancy within tumor and tumor margin areas. In comparison, for dysplastic lesions, OCT-based detection showed a sensitivity of 92.5% and specificity of 68.8% and a moderate concordance with histopathology diagnosis (ĸ = 0.59). Additionally, the OCT scores could significantly differentiate squamous cell carcinoma (SCC) from dysplastic lesions (mild/moderate/severe; p ≤ 0.005) as well as the latter from the non-dysplastic lesions (p ≤ 0.05). CONCLUSION: The current challenges associated with clinical examination-based margin assessment could be improved with intra-operative OCT imaging. OCT is capable of identifying microscopic tumor at the surgical margins and demonstrated the feasibility of mapping of field cancerization around the tumor.

Effects of a Dental Gel Over 6 Months on Periodontal Health in Subjects with Stage II and III (Mild and Moderate) Periodontitis.

OBJECTIVE: Overall aim of this prospective, randomized, positive controlled, double-blind in vivo study was to identify the effects of a test dental gel containing 2.6% edathamil with an added carrier and permeability enhancer vs. a positive control dentifrice on periodontal health measures in patients with Stage II and III periodontitis. METHODS: In this prospective double-blinded, randomized study, 33 subjects were randomly assigned in a 1:1 ratio to brushing their teeth with either the test gel (LivFresh®, Livionex Dental Gel, Los Gatos, CA 95030) or the positive control toothpaste (Crest ProHealth®, P&G, Cincinnati, OH 45202).Full-mouth gingival index, modified sulcus bleeding index, and periodontal pocket probing depths were recorded for all teeth at baseline, and on days 90 and 180.Subjects brushed with the study material twice a day. RESULTS: The test dental gel reduced gingival inflammation and bleeding, as well as periodontal pocket probing depths significantly more than a control dentifrice. CONCLUSIONS: In this pilot study in subjects with Stage II and III periodontitis, a test dental gel was found to improve gingival inflammation and bleeding, as well as periodontal pocket depths significantly better than a control dentifrice.